TL;DR
An interactive multimodal scene understanding framework based on frequency-domain dynamic routing and activation-region guidance, aiming to enhance multimodal feature representation for semantic segmentation and object detection, consistently outperforms existing approaches in image fusion, semantic segmentation, and object detection.
STSFusion is proposed, a segmentation task-driven spatial-frequency collaborative fusion method for infrared and visible images that achieves superior fusion performance over state-of-the-art methods and shows favorable task adaptability in downstream semantic segmentation and object detection.
A complementary prototype representation framework is proposed, employing three modules to collaboratively improve pseudo-label quality and improves the discriminative ability of confused categories by generating semantically similar sub-category negative samples.
Extensive experiments conducted on the public multispectral pedestrian detection KAIST dataset show that TFNet outperforms existing state-of-the-art algorithms and achieves a steady and substantial reduction in miss rate on the occlusion subset and multiscale subset.
The proposed Multi-scale Attention Enhancement Feature Fusion Network (MAEF-Net) systematically optimizes the overall detection pipeline from three aspects, namely feature enhancement, feature fusion, and prediction refinement, and achieves a favorable balance between detection accuracy and computational efficiency whi...
Enhanced Feature-Aware YOLO (EFA-YOLO), a lightweight detection framework based on YOLO11n, achieves a favorable balance between detection accuracy and computational efficiency, demonstrating real-time performance on the evaluated GPU platform and indicating potential for further edge-oriented optimization.
Object detection using visible–infrared images has become increasingly important for all-day detection scenarios. However, due to significant imaging discrepancies between the visible and infrared modalities, achieving accurate modal alignment and effective feature fusion remains a major challenge. Existing methods oft...
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